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Computational structural-based GPCR optimization for user-defined ligand: Implications for the development of

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Summary

This study introduces a computational method to engineer yeast biosensors by modifying the native Ste2 receptor to recognize new ligands like epinephrine. This approach enables custom biosensor development without prior knowledge of specific receptors or G proteins.

Keywords:
Bio-sensorsGPCRProtein engineering

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Area of Science:

  • Synthetic biology
  • Biochemistry
  • Computational biology

Background:

  • Organisms utilize biological machinery for environmental sensing.
  • Biosensors leverage natural systems to detect user-defined molecules.
  • The Saccharomyces cerevisiae pheromone pathway, using the G-Protein Coupled Receptor (GPCR) Ste2, is a candidate for synthetic signaling.

Purpose of the Study:

  • To develop a computational procedure for engineering native yeast GPCRs to recognize user-defined ligands.
  • To enable the creation of novel Saccharomyces cerevisiae-based biosensors without prior knowledge of ligand-receptor or receptor-G protein interactions.
  • To computationally design amino acid substitutions in Ste2 to recognize epinephrine.

Main Methods:

  • Utilized geometrical and chemical optimization of protein binding pockets.
  • Employed Monte Carlo simulations to design Ste2 binding pocket modifications.
  • Validated designed Ste2 mutants using molecular docking and molecular dynamics simulations.

Main Results:

  • Identified specific amino acid substitutions to enable Ste2 to recognize epinephrine.
  • Confirmed Ste2 mutants can accommodate and bind firmly to epinephrine.
  • Demonstrated efficient sampling of potential mutants, validating the computational strategy.

Conclusions:

  • The proposed computational method is a promising approach for developing new Saccharomyces cerevisiae-based biosensors.
  • This strategy allows for the engineering of yeast to detect a wide range of custom ligands.
  • The findings pave the way for versatile biosensor development through protein engineering.